You can find videos and slides for each below. Main message is that the machine learning workflow is not that simple.

MLConf, San Francisco

That was a great event. I was in very good company with top presenters from a number of prominent companies, as you can see from the speakers page. One key takeaway (not a surprise for me) is that machine learning is not all about deep learning. Sure, deep learning is used, but other techniques such as factorization machines and gradient boosted decision trees play a significant role in some very visible applications of machine learning as well.

I encourage readers to take a look at the videos of MLConf presentations. Here is information about mine:

My Abstract:

Why Machine Learning Algorithms Fall Short (And What You Can Do About It): Many think that machine learning is all about the algorithms. Want a self-learning system? Get your data, start coding or hire a PhD that will build you a model that will stand the test of time. Of course we know that this is not enough. Models degrade over time, algorithms that work great on yesterday’s data may not be the best option, new data sources and types are made available. In short, your self-learning system may not be learning anything at all. In this session, we will examine how to overcome challenges in creating self-learning systems that perform better and are built to stand the test of time. We will show how to apply mathematical optimization algorithms that often prove superior to local optimization methods favored by typical machine learning applications and discuss why these methods can crate better results. We will also examine the role of smart automation in the context of machine learning and how smart automation can create self-learning systems that are built to last.

Spark Summit Meetup, Brussels

At the recent sold-out Spark & Machine Learning Meetup in Brussels, I teamed up with Nick Pentreath of the Spark Technology Center to deliver the main talk of the meetup: Creating an end-to-end Recommender System with Apache Spark and Elasticsearch.

Nick did most of the talk, presenting how to build a recommender system. I talked about 10-15 minutes at the end, discussing the machine learning workflow and typical pain points within it.